Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929401190416384 |
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| author | Moslem, Yasmin |
| author_facet | Moslem, Yasmin |
| contents | This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17363 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation Moslem, Yasmin Computation and Language Sound Audio and Speech Processing This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity. |
| title | Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.17363 |